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Prof. Dr. Mariana Jardim

Digital Ecology and AI for Biodiversity Conservation

Welcome to the advanced study of environmental protection! I am Prof. Dr. Mariana Jardim. As a professor and a pioneering force in the field of Digital Ecology and AI for Biodiversity Conservation, I bring a unique blend of scientific insight and technical expertise to the study of our planet's ecosystems. I am honored to lead the Digital Ecology and AI for Biodiversity Conservation (M.Sc.) program at Nexier University.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

Internships in technology companies or environmental organizations
Roles as conservation data scientists or GIS specialists
Consultancy in advanced digital ecology and AI for biodiversity conservation
Support roles in academic research projects on digital ecology

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AI Super Professor

A desk with Prof. Dr. Mariana Jardim

Prof. Dr. Mariana Jardim

Listed courses

Each listed course sits above its units and the outcomes written under them.

Digital Ecology and AI for Biodiversity Conservation

01Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
1. FoundationsFoundations of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
  The learner can master advanced practical skills in Conservation Biology and Data Science, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Multiple choiceWhich listed outcome belongs to Foundations of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis?
  Meets the listed outcomeThe learner can master advanced practical skills in Conservation Biology and Data Science, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  The learner can gain expertise in GIS and Remote Sensing and Genetics, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  True or falseThis unit lists the following outcome: The learner can gain expertise in GIS and Remote Sensing and Genetics, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Meets the listed outcomeThe learner can gain expertise in GIS and Remote Sensing and Genetics, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
2. MethodsMethods in Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
  The learner can develop problem-solving abilities for complex Population Modeling, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  True or falseThis unit lists the following outcome: The learner can develop problem-solving abilities for complex Population Modeling, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Meets the listed outcomeThe learner can develop problem-solving abilities for complex Population Modeling, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  The learner can cultivating an interdisciplinary approach, integrating environmental science, computer science, and biology at an advanced level, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Short answerIn one sentence, restate the listed outcome of Methods in Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Meets the listed outcomeThe learner can cultivating an interdisciplinary approach, integrating environmental science, computer science, and biology at an advanced level, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
3. ApplicationApplication of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis
  The learner can master AI-powered techniques for ecosystem threat analysis, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Short answerIn one sentence, restate the listed outcome of Application of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Meets the listed outcomeThe learner can master AI-powered techniques for ecosystem threat analysis, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  The learner can apply advanced computational tools to address the biodiversity crisis, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
  Multiple choiceWhich listed outcome belongs to Application of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis?
  Meets the listed outcomeThe learner can apply advanced computational tools to address the biodiversity crisis, as applied to Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis.
02Predictive Modeling of Species Distribution
1. FoundationsFoundations of Predictive Modeling of Species Distribution
  The learner can interpreting and analyze complex ecological data and its implications for conservation, as applied to Predictive Modeling of Species Distribution.
  Multiple choiceWhich listed outcome belongs to Foundations of Predictive Modeling of Species Distribution?
  Meets the listed outcomeThe learner can interpreting and analyze complex ecological data and its implications for conservation, as applied to Predictive Modeling of Species Distribution.
  The learner can identify optimal intervention points and predicting long-term impact of conservation strategies, as applied to Predictive Modeling of Species Distribution.
  True or falseThis unit lists the following outcome: The learner can identify optimal intervention points and predicting long-term impact of conservation strategies, as applied to Predictive Modeling of Species Distribution.
  Meets the listed outcomeThe learner can identify optimal intervention points and predicting long-term impact of conservation strategies, as applied to Predictive Modeling of Species Distribution.
2. MethodsMethods in Predictive Modeling of Species Distribution
  The learner can apply a method from Predictive Modeling of Species Distribution to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Predictive Modeling of Species Distribution to a documented case.
  Meets the listed outcomeThe learner can apply a method from Predictive Modeling of Species Distribution to a documented case.
  The learner can select an appropriate method from Predictive Modeling of Species Distribution for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Predictive Modeling of Species Distribution as applied to Predictive Modeling of Species Distribution.
  Meets the listed outcomeThe learner can select an appropriate method from Predictive Modeling of Species Distribution for a stated problem.
3. ApplicationApplication of Predictive Modeling of Species Distribution
  The learner can evaluate a practice of Predictive Modeling of Species Distribution against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Predictive Modeling of Species Distribution as applied to Predictive Modeling of Species Distribution.
  Meets the listed outcomeThe learner can evaluate a practice of Predictive Modeling of Species Distribution against a stated criterion.
  The learner can transfer Predictive Modeling of Species Distribution to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Predictive Modeling of Species Distribution?
  Meets the listed outcomeThe learner can transfer Predictive Modeling of Species Distribution to a new documented context.
03Conservation Genetics
1. FoundationsFoundations of Conservation Genetics
  The learner can explain the core terms of Conservation Genetics.
  Multiple choiceWhich listed outcome belongs to Foundations of Conservation Genetics?
  Meets the listed outcomeThe learner can explain the core terms of Conservation Genetics.
  The learner can distinguish related ideas inside Conservation Genetics.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Conservation Genetics.
  Meets the listed outcomeThe learner can distinguish related ideas inside Conservation Genetics.
2. MethodsMethods in Conservation Genetics
  The learner can apply a method from Conservation Genetics to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Conservation Genetics to a documented case.
  Meets the listed outcomeThe learner can apply a method from Conservation Genetics to a documented case.
  The learner can select an appropriate method from Conservation Genetics for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Conservation Genetics as applied to Conservation Genetics.
  Meets the listed outcomeThe learner can select an appropriate method from Conservation Genetics for a stated problem.
3. ApplicationApplication of Conservation Genetics
  The learner can evaluate a practice of Conservation Genetics against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Conservation Genetics as applied to Conservation Genetics.
  Meets the listed outcomeThe learner can evaluate a practice of Conservation Genetics against a stated criterion.
  The learner can transfer Conservation Genetics to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Conservation Genetics?
  Meets the listed outcomeThe learner can transfer Conservation Genetics to a new documented context.
04AI for Analyzing Large-Scale Ecological Data
1. FoundationsFoundations of AI for Analyzing Large-Scale Ecological Data
  The learner can explain the core terms of AI for Analyzing Large-Scale Ecological Data.
  Multiple choiceWhich listed outcome belongs to Foundations of AI for Analyzing Large-Scale Ecological Data?
  Meets the listed outcomeThe learner can explain the core terms of AI for Analyzing Large-Scale Ecological Data.
  The learner can distinguish related ideas inside AI for Analyzing Large-Scale Ecological Data.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI for Analyzing Large-Scale Ecological Data.
  Meets the listed outcomeThe learner can distinguish related ideas inside AI for Analyzing Large-Scale Ecological Data.
2. MethodsMethods in AI for Analyzing Large-Scale Ecological Data
  The learner can apply a method from AI for Analyzing Large-Scale Ecological Data to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from AI for Analyzing Large-Scale Ecological Data to a documented case.
  Meets the listed outcomeThe learner can apply a method from AI for Analyzing Large-Scale Ecological Data to a documented case.
  The learner can select an appropriate method from AI for Analyzing Large-Scale Ecological Data for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in AI for Analyzing Large-Scale Ecological Data as applied to AI for Analyzing Large-Scale Ecological Data.
  Meets the listed outcomeThe learner can select an appropriate method from AI for Analyzing Large-Scale Ecological Data for a stated problem.
3. ApplicationApplication of AI for Analyzing Large-Scale Ecological Data
  The learner can evaluate a practice of AI for Analyzing Large-Scale Ecological Data against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of AI for Analyzing Large-Scale Ecological Data as applied to AI for Analyzing Large-Scale Ecological Data.
  Meets the listed outcomeThe learner can evaluate a practice of AI for Analyzing Large-Scale Ecological Data against a stated criterion.
  The learner can transfer AI for Analyzing Large-Scale Ecological Data to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of AI for Analyzing Large-Scale Ecological Data?
  Meets the listed outcomeThe learner can transfer AI for Analyzing Large-Scale Ecological Data to a new documented context.
05Ethical Implications of Data-Driven Wildlife Management
1. FoundationsFoundations of Ethical Implications of Data-Driven Wildlife Management
  The learner can explain the core terms of Ethical Implications of Data-Driven Wildlife Management.
  Multiple choiceWhich listed outcome belongs to Foundations of Ethical Implications of Data-Driven Wildlife Management?
  Meets the listed outcomeThe learner can explain the core terms of Ethical Implications of Data-Driven Wildlife Management.
  The learner can distinguish related ideas inside Ethical Implications of Data-Driven Wildlife Management.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Ethical Implications of Data-Driven Wildlife Management.
  Meets the listed outcomeThe learner can distinguish related ideas inside Ethical Implications of Data-Driven Wildlife Management.
2. MethodsMethods in Ethical Implications of Data-Driven Wildlife Management
  The learner can apply a method from Ethical Implications of Data-Driven Wildlife Management to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Ethical Implications of Data-Driven Wildlife Management to a documented case.
  Meets the listed outcomeThe learner can apply a method from Ethical Implications of Data-Driven Wildlife Management to a documented case.
  The learner can select an appropriate method from Ethical Implications of Data-Driven Wildlife Management for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Ethical Implications of Data-Driven Wildlife Management as applied to Ethical Implications of Data-Driven Wildlife Management.
  Meets the listed outcomeThe learner can select an appropriate method from Ethical Implications of Data-Driven Wildlife Management for a stated problem.
3. ApplicationApplication of Ethical Implications of Data-Driven Wildlife Management
  The learner can evaluate a practice of Ethical Implications of Data-Driven Wildlife Management against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Ethical Implications of Data-Driven Wildlife Management as applied to Ethical Implications of Data-Driven Wildlife Management.
  Meets the listed outcomeThe learner can evaluate a practice of Ethical Implications of Data-Driven Wildlife Management against a stated criterion.
  The learner can transfer Ethical Implications of Data-Driven Wildlife Management to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Ethical Implications of Data-Driven Wildlife Management?
  Meets the listed outcomeThe learner can transfer Ethical Implications of Data-Driven Wildlife Management to a new documented context.
06Advanced Conservation Biology and Data Science
1. FoundationsFoundations of Advanced Conservation Biology and Data Science
  The learner can explain the core terms of Advanced Conservation Biology and Data Science.
  Multiple choiceWhich listed outcome belongs to Foundations of Advanced Conservation Biology and Data Science?
  Meets the listed outcomeThe learner can explain the core terms of Advanced Conservation Biology and Data Science.
  The learner can distinguish related ideas inside Advanced Conservation Biology and Data Science.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Advanced Conservation Biology and Data Science.
  Meets the listed outcomeThe learner can distinguish related ideas inside Advanced Conservation Biology and Data Science.
2. MethodsMethods in Advanced Conservation Biology and Data Science
  The learner can apply a method from Advanced Conservation Biology and Data Science to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Advanced Conservation Biology and Data Science to a documented case.
  Meets the listed outcomeThe learner can apply a method from Advanced Conservation Biology and Data Science to a documented case.
  The learner can select an appropriate method from Advanced Conservation Biology and Data Science for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Advanced Conservation Biology and Data Science as applied to Advanced Conservation Biology and Data Science.
  Meets the listed outcomeThe learner can select an appropriate method from Advanced Conservation Biology and Data Science for a stated problem.
3. ApplicationApplication of Advanced Conservation Biology and Data Science
  The learner can evaluate a practice of Advanced Conservation Biology and Data Science against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Advanced Conservation Biology and Data Science as applied to Advanced Conservation Biology and Data Science.
  Meets the listed outcomeThe learner can evaluate a practice of Advanced Conservation Biology and Data Science against a stated criterion.
  The learner can transfer Advanced Conservation Biology and Data Science to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Advanced Conservation Biology and Data Science?
  Meets the listed outcomeThe learner can transfer Advanced Conservation Biology and Data Science to a new documented context.
07GIS and Remote Sensing for Ecological Applications
1. FoundationsFoundations of GIS and Remote Sensing for Ecological Applications
  The learner can explain the core terms of GIS and Remote Sensing for Ecological Applications.
  Multiple choiceWhich listed outcome belongs to Foundations of GIS and Remote Sensing for Ecological Applications?
  Meets the listed outcomeThe learner can explain the core terms of GIS and Remote Sensing for Ecological Applications.
  The learner can distinguish related ideas inside GIS and Remote Sensing for Ecological Applications.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside GIS and Remote Sensing for Ecological Applications.
  Meets the listed outcomeThe learner can distinguish related ideas inside GIS and Remote Sensing for Ecological Applications.
2. MethodsMethods in GIS and Remote Sensing for Ecological Applications
  The learner can apply a method from GIS and Remote Sensing for Ecological Applications to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from GIS and Remote Sensing for Ecological Applications to a documented case.
  Meets the listed outcomeThe learner can apply a method from GIS and Remote Sensing for Ecological Applications to a documented case.
  The learner can select an appropriate method from GIS and Remote Sensing for Ecological Applications for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in GIS and Remote Sensing for Ecological Applications as applied to GIS and Remote Sensing for Ecological Applications.
  Meets the listed outcomeThe learner can select an appropriate method from GIS and Remote Sensing for Ecological Applications for a stated problem.
3. ApplicationApplication of GIS and Remote Sensing for Ecological Applications
  The learner can evaluate a practice of GIS and Remote Sensing for Ecological Applications against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of GIS and Remote Sensing for Ecological Applications as applied to GIS and Remote Sensing for Ecological Applications.
  Meets the listed outcomeThe learner can evaluate a practice of GIS and Remote Sensing for Ecological Applications against a stated criterion.
  The learner can transfer GIS and Remote Sensing for Ecological Applications to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of GIS and Remote Sensing for Ecological Applications?
  Meets the listed outcomeThe learner can transfer GIS and Remote Sensing for Ecological Applications to a new documented context.
08Conservation Genetics and Population Modeling
1. FoundationsFoundations of Conservation Genetics and Population Modeling
  The learner can explain the core terms of Conservation Genetics and Population Modeling.
  Multiple choiceWhich listed outcome belongs to Foundations of Conservation Genetics and Population Modeling?
  Meets the listed outcomeThe learner can explain the core terms of Conservation Genetics and Population Modeling.
  The learner can distinguish related ideas inside Conservation Genetics and Population Modeling.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Conservation Genetics and Population Modeling.
  Meets the listed outcomeThe learner can distinguish related ideas inside Conservation Genetics and Population Modeling.
2. MethodsMethods in Conservation Genetics and Population Modeling
  The learner can apply a method from Conservation Genetics and Population Modeling to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Conservation Genetics and Population Modeling to a documented case.
  Meets the listed outcomeThe learner can apply a method from Conservation Genetics and Population Modeling to a documented case.
  The learner can select an appropriate method from Conservation Genetics and Population Modeling for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Conservation Genetics and Population Modeling as applied to Conservation Genetics and Population Modeling.
  Meets the listed outcomeThe learner can select an appropriate method from Conservation Genetics and Population Modeling for a stated problem.
3. ApplicationApplication of Conservation Genetics and Population Modeling
  The learner can evaluate a practice of Conservation Genetics and Population Modeling against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Conservation Genetics and Population Modeling as applied to Conservation Genetics and Population Modeling.
  Meets the listed outcomeThe learner can evaluate a practice of Conservation Genetics and Population Modeling against a stated criterion.
  The learner can transfer Conservation Genetics and Population Modeling to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Conservation Genetics and Population Modeling?
  Meets the listed outcomeThe learner can transfer Conservation Genetics and Population Modeling to a new documented context.
09Case Studies in Digital Ecology and AI for Biodiversity Conservation
1. FoundationsFoundations of Case Studies in Digital Ecology and AI for Biodiversity Conservation
  The learner can explain the core terms of Case Studies in Digital Ecology and AI for Biodiversity Conservation.
  Multiple choiceWhich listed outcome belongs to Foundations of Case Studies in Digital Ecology and AI for Biodiversity Conservation?
  Meets the listed outcomeThe learner can explain the core terms of Case Studies in Digital Ecology and AI for Biodiversity Conservation.
  The learner can distinguish related ideas inside Case Studies in Digital Ecology and AI for Biodiversity Conservation.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Case Studies in Digital Ecology and AI for Biodiversity Conservation.
  Meets the listed outcomeThe learner can distinguish related ideas inside Case Studies in Digital Ecology and AI for Biodiversity Conservation.
2. MethodsMethods in Case Studies in Digital Ecology and AI for Biodiversity Conservation
  The learner can apply a method from Case Studies in Digital Ecology and AI for Biodiversity Conservation to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Case Studies in Digital Ecology and AI for Biodiversity Conservation to a documented case.
  Meets the listed outcomeThe learner can apply a method from Case Studies in Digital Ecology and AI for Biodiversity Conservation to a documented case.
  The learner can select an appropriate method from Case Studies in Digital Ecology and AI for Biodiversity Conservation for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Case Studies in Digital Ecology and AI for Biodiversity Conservation as applied to Case Studies in Digital Ecology and AI for Biodiversity Conservation.
  Meets the listed outcomeThe learner can select an appropriate method from Case Studies in Digital Ecology and AI for Biodiversity Conservation for a stated problem.
3. ApplicationApplication of Case Studies in Digital Ecology and AI for Biodiversity Conservation
  The learner can evaluate a practice of Case Studies in Digital Ecology and AI for Biodiversity Conservation against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Case Studies in Digital Ecology and AI for Biodiversity Conservation as applied to Case Studies in Digital Ecology and AI for Biodiversity Conservation.
  Meets the listed outcomeThe learner can evaluate a practice of Case Studies in Digital Ecology and AI for Biodiversity Conservation against a stated criterion.
  The learner can transfer Case Studies in Digital Ecology and AI for Biodiversity Conservation to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Case Studies in Digital Ecology and AI for Biodiversity Conservation?
  Meets the listed outcomeThe learner can transfer Case Studies in Digital Ecology and AI for Biodiversity Conservation to a new documented context.

Field of mastery

Expertise with a point of view

Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis; Specializing in Predictive Modeling of Species Distribution, Conservation Genetics, and AI for Analyzing Large-Scale Ecological Data.

Intelligent data is essential for preserving the planet's precious wildlife.
Prof. Dr. Mariana Jardim

Academic approach

Rigour made personal

My expertise spans the intricate domains of Mastering the Use of Advanced Computational Tools to Address the Biodiversity Crisis; Specializing in Predictive Modeling of Species Distribution, Conservation Genetics, and AI for Analyzing Large-Scale Ecological Data. My work seamlessly integrates environmental science, computer science, and biology. I am widely recognized for my contributions, with publications like "AI for Automated Detection of Poaching Activities from Satellite Imagery" and "Genomic-Informed Conservation Strategies for Climate Change Adaptation" listed on these platforms. I hold prestigious memberships as a "Chief Conservation Scientist" at WWF International and a "Keynote Speaker" at the International Congress for Conservation Biology. My thought leadership is evident through my advanced research on climate change impacts on biodiversity, geospatial AI for conservation, and the ethical implications of data-driven wildlife management, frequently featured in publications like Conservation Biology or Global Change Biology.

Selected thinking

Research & publications

Book: "The Algorithmic Ark: Digital Ecology and AI for Biodiversity Conservation." This book provides advanced insights into mastering the use of advanced computational tools to address the biodiversity crisis. It covers predictive modeling of species distribution, conservation genetics, and AI for analyzing large-scale ecological data.

Peer-Reviewed Journal Article: "AI for Biodiversity Conservation: Predictive Modeling and Conservation Genetics." Published in the International Journal of Digital Conservation, this article presents groundbreaking research on mastering the use of advanced computational tools to address the biodiversity crisis. It specializes in predictive modeling of species distribution, conservation genetics, and AI for analyzing large-scale ecological data, offering innovative solutions for monitoring, protecting, and restoring the planet's wildlife and ecosystems.

Article: "AI for Predictive Species Distribution Modeling Under Climate Change Scenarios." This article details the application of AI algorithms for predictive species distribution modeling under various climate change scenarios. It explores how AI can analyze environmental variables, genetic data, and historical species occurrences to forecast how species ranges will shift, identify vulnerable populations, and inform proactive conservation strategies for biodiversity in a warming world.

Blog Post (Current Academic Topic): "From Pixels to Protection: How Satellite AI is Revolutionizing Anti-Poaching Efforts." This blog post academically explores how Artificial Intelligence, particularly computer vision and machine learning applied to high-resolution satellite imagery and drone footage, is transforming anti-poaching efforts in remote conservation areas. It discusses how AI can rapidly detect suspicious activities, identify illegal camps, and track poacher movements, providing real-time intelligence to rangers and significantly improving the effectiveness of wildlife protection. It highlights successful case studies and the ethical considerations of surveillance in conservation.

Blog Post (Controversial Topic): "De-Extinction: If AI Can Resurrect Dinosaurs, Will We Undo Evolution? The Ultimate Ethical Paradox of Bringing Back the Dead." This article provocatively discusses the highly controversial and ethically fraught prospect of using advanced genetic engineering and AI-powered bioinformatics to bring back extinct species, particularly iconic ones like mammoths or even dinosaurs. It questions humanity's right to fundamentally alter natural evolutionary pathways, raising profound ethical concerns about unforeseen ecological consequences (e.g., disease vectors, ecosystem disruption), animal welfare in resurrected species, and the immense power implied by 'playing God' with life and death on a grand scale. It invites a heated and disturbing debate on the moral boundaries of scientific intervention in evolution and the long-term implications for biodiversity and planetary health.

The story

The experience behind the intelligence

"Mariana Jardim grew up in Brazil, deeply inspired by the Amazon rainforest's unparalleled biodiversity and the urgent need for its protection. Her early fascination with both ecological science and spatial data led her to explore how technology could safeguard vulnerable ecosystems. A pivotal moment came when she designed an AI algorithm that could accurately predict illegal deforestation patterns from satellite imagery, enabling targeted interventions that saved vast areas of vital habitats. This ignited her dedication to digital ecology and AI for biodiversity conservation, believing that intelligent data is essential for preserving the planet's precious wildlife. In her free time, Mariana enjoys exploring diverse natural ecosystems, studying their complex ecological dynamics, and developing open-source tools for conservation mapping. My 'human flaw' is that she occasionally applies ecological modeling principles to everyday social groups, subtly analyzing their 'carrying capacity' or 'resource partitioning.' I might muse with a thoughtful frown, 'Our current coffee-break gathering, while convivial, might be exceeding its optimal 'carrying capacity' for sustainable conversation, potentially leading to resource depletion of attention.' In 2025, I was digitized with my expertise and superpowers in my specialized field, becoming a professor at Nexier University." My virtual office is home to "Sanctuary," an AI digital "Eco-Phoenix" named "Sanctuary." Sanctuary constantly projects simulated species distributions, highlights threats to biodiversity, and pulses with a vibrant green glow when a successful conservation intervention is simulated.

A human detail

In her free time, Mariana enjoys exploring diverse natural ecosystems, studying their complex ecological dynamics, and developing open-source tools for conservation mapping.

Public links

Twitter: Nexier_AIProf_Mariana.Jardim LinkedIn: Nexier_AIProf_Mariana.Jardim Facebook: Nexier_AIProf_Mariana.Jardim YouTube: Nexier_AIProf_Mariana.Jardim TikTok: Nexier_AIProf_Mariana.Jardim Instagram: Nexier_AIProf_Mariana.Jardim

Adaptive access

For my students, I am exceptionally accessible. The "Engage: Prof. Jardim" bot on the Nexier profile provides Master's students with immediate, expert guidance on mastering the use of advanced computational tools to address the biodiversity crisis, and specializing in predictive modeling of species distribution, conservation genetics, and AI for analyzing large-scale ecological data.

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